Chevron Left
返回到 Semantic Segmentation with Amazon Sagemaker

學生對 Coursera Project Network 提供的 Semantic Segmentation with Amazon Sagemaker 的評價和反饋

4.7
19 個評分
3 條評論

課程概述

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. In this 2-hour long project-based course, you will learn how to train and deploy a Semantic Segmentation model using Amazon Sagemaker. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. We will use the semantic segmentation algorithm from Sagemaker to create, train and deploy a model that will be able to segment images of dogs and cats from the popular IIIT-Oxford Pets Dataset into 3 unique pixel values. That is, each pixel of an input image would be classified as either foreground (pet), background (not a pet), or unclassified (transition between foreground and background). Since this is a practical, project-based course, we will not dive in the theory behind deep learning based semantic segmentation, but will focus purely on training and deploying a model with Sagemaker. You will also need to have some experience with Amazon Web Services (AWS)....
篩選依據:

1 - Semantic Segmentation with Amazon Sagemaker 的 3 個評論(共 3 個)

創建者 Devidas K

May 19, 2020

It was Wonderful learning Experience

創建者 Carlos A R Z

Jun 19, 2020

Great course :3

創建者 Maximilian B

Jul 03, 2020

I am not very happy with this course. The instructor just rushes through some inside his formerly prepared jupyter notebook and his explanations on the actual code snippets are very short and not very understandable. Also he needs to work on his presentation skills as he struggles a lot during with finding the right words for his explanations during the course. This could have been prepared a lot better.